Present embodiments relate to the field of dataset shift in machine learning.
Dataset shift is a concept in machine learning in which the characteristics of the training data differ in some way from the characteristics of the test data. Dataset shift is present in many practical applications, for reasons ranging from the bias introduced by experimental design to the irreproducibility of the testing conditions at training time. Dataset shift often causes degradation in recognition and classification accuracy. Given that, it has received a growing amount of interest in the last few years.
The foregoing examples of the related art and limitations related therewith are intended to be illustrative and not exclusive. Other limitations of the related art will become apparent to those of skill in the art upon a reading of the specification and a study of the figures.